Diffusion tensor MRI (DTI) is a powerful in vivo technique that is sensitive to deep brain tissue water microdynamics and microstructure. DTI-derived orientation and scalar maps have the unique potential to provide objective and specific measures of the Multiple Sclerosis pathology. The hallmarks of MS pathology may include inflammation, demyelination, gliosis, direct axonal loss directly or indirectly through Wallerian degeneration (WD). WD can cause axonal loss distal from the initial demyelinating lesion and its signature in MS has not been elucidated using a comprehensive DTI and conventional MRI approach. The Corpus callosum (CC), pyramidal, corticospinal tracts coursing through the internal capsule (IC) are important structures that are implicated in neurological deficit in MS. Unfortunately, the limited published literature on DTI of MS in these structures is often inconsistent and sometimes contradictory. Based on our preliminary studies, these inconsistencies and contradictions, at least in part, could be attributed to sub-optimal acquisition schemes, arbitrary region of interest placement, failure to recognize the regional heterogeneity of these structures, the age and gender dependence of DTI measure. In order to overcome some of these limitations, we propose to acquire DTI data at 3.0 T using parallel imaging at different age groups on normal males and females. In addition, MRI data will also be acquired on MS subjects. The DTI data will be acquired from the whole brain using optimized Icosa21 scheme that is shown to be balanced and unbiased. Specifically we will concentrate on the CC, pyramidal and CST tracts which are implicated in MS, to identify WD signature in MS. We will divide the corpus callosum into seven functionally distinct sub regions. Similarly the internal capsule will be divided into four quadrants and its temporal-spatial correlations will be followed longitudinally. DTI values will be derived from each one of these individual structures. A robust DTI analysis tool will be developed for automatic analysis. This tool will also help in the fusion of multi-modal MRI data for a robust segmentation of the subregions of CC and 1C. The DTI measures, after accounting for the age and gender dependence will be correlated with the clinical measures.